def __init__(self, hparams):
super().__init__()
self.hparams = hparams
# 让每次模型初始化一致, 不让只要中间有再次初始化的情况, 结果立马跑偏
seed_reproducer(self.hparams.seed)
self.model = se_resnext50_32x4d()
self.criterion = CrossEntropyLossOneHot()
self.logger_kun = init_logger("kun_in", hparams.log_dir)
def __init__(self, hparams):
super().__init__()
self.hparams = hparams
# 让每次模型初始化一致, 不让只要中间有再次初始化的情况, 结果立马跑偏
seed_reproducer(self.hparams.seed)
# [efficientnet-b1, se_resnext50_32x4d, se_resnet50]
self.model = CassavaModel(
self.hparams.backbone
) if self.hparams.backbone == 'se_resnext50_32x4d' else CassavaModelTimm(
self.hparams.backbone)
self.criterion = torch.nn.CrossEntropyLoss(
) if not self.hparams.onehot else CrossEntropyLossOneHot()
weight = torch.as_tensor([1., 1., 1., 1., 1.])
# self.criterion = torch.nn.BCEWithLogitsLoss(weight=None, reduce=None, reduction='mean', pos_weight=weight)
self.logger_kun = init_logger("kun_in",
f'{hparams.log_dir}/{hparams.version}')
self.fmix = FMix(decay_power=self.hparams.fmix_delta,
alpha=self.hparams.fmix_beta,
size=(int(self.hparams.image_size[1]),
self.hparams.image_size[1]),
max_soft=0.0,
reformulate=False)
# Third party libraries
import torch
from dataset import generate_transforms
from sklearn.model_selection import KFold
from scipy.special import softmax
from torch.utils.data import DataLoader
from tqdm import tqdm
# User defined libraries
from train import CoolSystem
from utils import init_hparams, init_logger, seed_reproducer, load_data
from dataset import PlantDataset
if __name__ == "__main__":
# Make experiment reproducible
seed_reproducer(2020)
# Init Hyperparameters
hparams = init_hparams()
# init logger
logger = init_logger("kun_out", log_dir=hparams.log_dir)
# Load data
data, test_data = load_data(logger)
# Generate transforms
transforms = generate_transforms(hparams.image_size)
early_stop_callback = EarlyStopping(monitor="val_roc_auc",
patience=10,
from torchvision import datasets
from torch.autograd import Variable
import torch.nn.functional as F
from tqdm import tqdm
import pandas as pd
import numpy as np
import os
import sys
import csv
from utils import parse_args, seed_reproducer
# Training settings
args = parse_args(sys.argv[1:])
use_cuda = torch.cuda.is_available()
seed_reproducer()
# Create experiment folder
if not os.path.isdir(args.experiment):
os.makedirs(args.experiment)
model_dir = os.path.join(args.experiment, 'checkpoints')
log_dir = os.path.join(args.experiment, 'logs')
if not os.path.isdir(model_dir):
os.makedirs(model_dir)
if not os.path.isdir(log_dir):
os.makedirs(log_dir)
from sklearn.model_selection import KFold
from data import BirdDataSetUnlabeled, BirdDataSetLabeled, return_data_transforms, mixup_data, return_data_test_transforms
from loss_function import alpha_weight, mixup_criterion, CenterLoss, LabelSmoothingCrossEntropy, linear_combination, reduce_loss
from model import Net, Net2